Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "3"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 3 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 3, Node N01:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460013 digital_ok 100.00% 0.00% 100.00% 0.00% - - 11.193853 15.507202 -0.034386 11.617946 5.662298 7.096236 -0.119659 2.220231 0.3155 0.0395 0.2579 nan nan
2460012 digital_ok 100.00% 0.00% 100.00% 0.00% - - 13.086973 14.549915 -0.105660 11.448944 5.868540 7.683555 0.736146 2.544700 0.3121 0.0396 0.2542 nan nan
2460011 digital_ok 100.00% 0.00% 100.00% 0.00% - - 13.762229 15.658167 -0.361735 15.327583 10.510953 15.890223 0.595657 2.235214 0.3132 0.0405 0.2462 nan nan
2460010 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.636325 17.079683 11.703031 12.692505 9.135963 10.435400 1.292592 1.916768 0.0272 0.0251 0.0020 nan nan
2460009 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.701371 15.828349 13.042572 13.971899 7.248571 8.800001 0.914140 2.056666 0.0284 0.0297 0.0021 nan nan
2460008 digital_ok 100.00% 100.00% 100.00% 0.00% - - 14.177344 19.315977 14.282526 15.384078 6.585775 7.744829 4.475085 5.442940 0.0293 0.0325 0.0039 nan nan
2460007 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.511609 14.457762 11.170376 12.030477 5.858751 7.187026 1.581698 2.256676 0.0288 0.0309 0.0028 nan nan
2459999 digital_ok 0.00% 100.00% 99.92% 0.00% - - nan nan nan nan nan nan nan nan 0.0285 0.0287 0.0017 nan nan
2459998 digital_ok 100.00% 100.00% 100.00% 0.00% - - 8.936531 12.266897 9.555272 10.177354 7.851481 10.152247 0.699884 1.555373 0.0294 0.0307 0.0022 nan nan
2459997 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.796582 13.373339 10.124122 10.937332 7.639939 9.568962 2.498469 3.252955 0.0288 0.0315 0.0031 nan nan
2459996 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.867127 14.402133 12.701402 13.387460 7.204115 9.215343 0.401677 1.028140 0.0295 0.0311 0.0024 nan nan
2459995 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.086859 14.607617 11.802178 12.586658 7.894341 9.422113 0.338363 0.870221 0.0308 0.0358 0.0054 nan nan
2459994 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.629772 14.156188 10.190057 11.027847 7.682337 9.491822 0.260149 0.600638 0.0297 0.0316 0.0027 nan nan
2459993 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.779451 13.304223 9.473089 10.227988 10.069923 10.885963 0.784779 2.118567 0.0286 0.0280 0.0011 nan nan
2459991 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.627323 16.501330 10.041473 10.823606 9.083055 10.725615 0.247238 0.602029 0.0295 0.0311 0.0024 nan nan
2459990 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.249252 13.591696 9.835656 10.513639 8.988087 11.003096 0.107125 0.333668 0.0306 0.0337 0.0039 nan nan
2459989 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.021929 13.798539 8.752737 9.610649 7.936443 9.224220 -0.112367 0.158846 0.0293 0.0307 0.0022 nan nan
2459988 digital_ok 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459987 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.873177 13.500845 9.828621 10.662675 6.319746 7.940419 0.727201 1.651845 0.0299 0.0326 0.0032 nan nan
2459986 digital_ok 100.00% 100.00% 100.00% 0.00% - - 12.378246 16.560663 10.768446 11.508187 9.285808 11.213822 5.424803 9.473313 0.0293 0.0314 0.0028 nan nan
2459985 digital_ok 100.00% 100.00% 100.00% 0.00% - - 11.343057 14.985243 9.974267 10.719051 7.156198 8.578457 1.038693 1.641559 0.0291 0.0310 0.0024 nan nan
2459984 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.726023 14.401620 10.321377 11.101668 9.391122 12.064181 2.367805 2.960199 0.0300 0.0328 0.0035 nan nan
2459983 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.567675 14.099277 9.910680 10.517037 9.193554 11.128224 2.818204 5.982450 0.0305 0.0325 0.0028 nan nan
2459982 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.194973 11.417059 8.409544 8.983374 4.479884 5.250528 2.389768 3.158011 0.0298 0.0315 0.0026 nan nan
2459981 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.866232 12.995503 10.569946 11.193192 10.344184 12.322539 0.254460 0.706532 0.0307 0.0337 0.0036 nan nan
2459980 digital_ok 100.00% 100.00% 100.00% 0.00% - - 9.692546 12.529826 9.495564 10.227343 8.931582 10.754345 5.064086 5.219430 0.0299 0.0327 0.0034 nan nan
2459979 digital_ok 100.00% 100.00% 100.00% 0.00% - - 10.071969 13.079559 8.801147 9.572651 8.862525 10.085197 0.372038 0.801409 0.0314 0.0318 0.0028 nan nan
2459978 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.177085 12.407852 9.557122 -0.813959 9.259305 4.199990 -0.004669 12.283545 0.0315 0.3253 0.2614 nan nan
2459977 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.462066 12.956305 9.382386 -0.149740 9.126398 7.392267 0.051887 5.689491 0.0329 0.3021 0.2371 nan nan
2459976 digital_ok 100.00% 100.00% 0.00% 0.00% - - 10.387247 13.216001 9.877424 -0.295822 9.318820 4.253448 0.826206 5.666309 0.0318 0.3308 0.2635 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 3: 2460013

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 15.507202 11.193853 15.507202 -0.034386 11.617946 5.662298 7.096236 -0.119659 2.220231

Antenna 3: 2460012

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.549915 13.086973 14.549915 -0.105660 11.448944 5.868540 7.683555 0.736146 2.544700

Antenna 3: 2460011

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Temporal Variability 15.890223 13.762229 15.658167 -0.361735 15.327583 10.510953 15.890223 0.595657 2.235214

Antenna 3: 2460010

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 17.079683 12.636325 17.079683 11.703031 12.692505 9.135963 10.435400 1.292592 1.916768

Antenna 3: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 15.828349 11.701371 15.828349 13.042572 13.971899 7.248571 8.800001 0.914140 2.056666

Antenna 3: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 19.315977 19.315977 14.177344 15.384078 14.282526 7.744829 6.585775 5.442940 4.475085

Antenna 3: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.457762 10.511609 14.457762 11.170376 12.030477 5.858751 7.187026 1.581698 2.256676

Antenna 3: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 3: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 12.266897 8.936531 12.266897 9.555272 10.177354 7.851481 10.152247 0.699884 1.555373

Antenna 3: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.373339 9.796582 13.373339 10.124122 10.937332 7.639939 9.568962 2.498469 3.252955

Antenna 3: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.402133 10.867127 14.402133 12.701402 13.387460 7.204115 9.215343 0.401677 1.028140

Antenna 3: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.607617 11.086859 14.607617 11.802178 12.586658 7.894341 9.422113 0.338363 0.870221

Antenna 3: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.156188 10.629772 14.156188 10.190057 11.027847 7.682337 9.491822 0.260149 0.600638

Antenna 3: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.304223 11.779451 13.304223 9.473089 10.227988 10.069923 10.885963 0.784779 2.118567

Antenna 3: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 16.501330 12.627323 16.501330 10.041473 10.823606 9.083055 10.725615 0.247238 0.602029

Antenna 3: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.591696 13.591696 10.249252 10.513639 9.835656 11.003096 8.988087 0.333668 0.107125

Antenna 3: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.798539 13.798539 10.021929 9.610649 8.752737 9.224220 7.936443 0.158846 -0.112367

Antenna 3: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape nan nan nan inf inf nan nan nan nan

Antenna 3: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.500845 9.873177 13.500845 9.828621 10.662675 6.319746 7.940419 0.727201 1.651845

Antenna 3: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 16.560663 16.560663 12.378246 11.508187 10.768446 11.213822 9.285808 9.473313 5.424803

Antenna 3: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.985243 14.985243 11.343057 10.719051 9.974267 8.578457 7.156198 1.641559 1.038693

Antenna 3: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.401620 10.726023 14.401620 10.321377 11.101668 9.391122 12.064181 2.367805 2.960199

Antenna 3: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 14.099277 10.567675 14.099277 9.910680 10.517037 9.193554 11.128224 2.818204 5.982450

Antenna 3: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 11.417059 9.194973 11.417059 8.409544 8.983374 4.479884 5.250528 2.389768 3.158011

Antenna 3: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 12.995503 12.995503 9.866232 11.193192 10.569946 12.322539 10.344184 0.706532 0.254460

Antenna 3: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 12.529826 12.529826 9.692546 10.227343 9.495564 10.754345 8.931582 5.219430 5.064086

Antenna 3: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.079559 10.071969 13.079559 8.801147 9.572651 8.862525 10.085197 0.372038 0.801409

Antenna 3: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 12.407852 12.407852 10.177085 -0.813959 9.557122 4.199990 9.259305 12.283545 -0.004669

Antenna 3: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 12.956305 10.462066 12.956305 9.382386 -0.149740 9.126398 7.392267 0.051887 5.689491

Antenna 3: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
3 N01 digital_ok nn Shape 13.216001 13.216001 10.387247 -0.295822 9.877424 4.253448 9.318820 5.666309 0.826206
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